Landslide and human mortality have been a common phenomenon in the Rangamati district over the past several years. This study examined the geotechnical properties (e.g., grain size analysis, ...plasticity index, liquid limit index) and geochemical properties (e.g., erodibility index, weathering index,
K
factor value, redness rating) of the soil in order to determine the causes and vulnerability of the landslide. Three types of soils (sand, silt and clay) have been classified based on grain-size distribution for geotechnical and geochemical analyses. The geotechnical properties of the soils examined indicate that most soils are sandy loams, of poor quality and plastic in nature. Geochemical properties show that the weathering index is higher for the clayey soil which is about 3.65 and the erosion index is higher found in the silty soil of ~ 6.7. The stability index is very low (~ 0.06), and the
K
factor value is higher 0.046 for the non-cohesive sandy soil which indicates high susceptibility of landslide. Numerical analysis based on geotechnical properties of the soil indicates that the steep slopes are even stable at the dry condition, while it is in risk at wet condition. A bio-engineering approach was proposed which showed that local plants could withstand and render stable in the barred slope in a few years.
Decontamination of pathogens on surfaces of substances is very important for controlling infectious diseases. In the present experiments, we tested various disinfectants in aqueous phase as well as ...on plastic surface carrying a viral inoculum, through dropping and wiping decontamination techniques, comparatively, so as to evaluate virucidal efficacies of those disinfectants toward an avian coronavirus (infectious bronchitis virus: IBV). We regard this evaluation system applicable to SARS-CoV-2. The disinfectants evaluated were 0.17% food additive glade calcium hydroxide (FdCa(OH)2) solution, sodium hypochlorite at 500 or 1,000 ppm of total chlorine (NaClO-500 or NaClO-1,000, respectively), NaClO at 500 ppm of total chlorine in 0.17% FdCa(OH)2 (Mix-500) and quaternary ammonium compound (QAC) diluted 500-fold in water (QAC-500). In the suspension test, all solutions inactivated IBV inoculum that contained 5% fetal bovine serum (FBS) under detectable level within 30 sec. In the carrier test, all solutions, except NaClO-500, could inactivate IBV with 0.5% FBS on a carrier to undetectable level in the wiping-sheets and wiped-carriers. We thus conclude that suspension and carrier tests should be introduced to evaluate disinfectants for the field usage, and that this evaluation system is important and workable for resultful selection of the tested disinfectants against avian coronavirus and SARS-CoV-2 on surfaces, particularly on plastic fomite.
Wearable sensors and biomedical devices have attracted a great deal of attention among users. Despite technological advancements in this field, a mixture of both progress and setbacks has been ...witnessed. The limited battery life of these devices for long‐term operation remains a major challenge. In this context, thermoelectric generators have emerged as potential candidates for harvesting energy from temperature gradients to power wearable sensors and devices. This review focuses on the working mechanism of a thermoelectric generator, as well as the current progress of a variety of promising and widely used inorganic and organic thermoelectric materials. Encouragingly, the highest ZT values of 2.27, 2.5, 2.8, 2.3, and 1.85 are obtained for bismuth telluride, lead telluride, tin selenide, copper selenide, and magnesium antimonide, respectively, at various temperature ranges. Meanwhile, organic materials such as poly (3,4‐ethylenedioxythiophene) polystyrene sulfonate, polyaniline composite, and graphdiyne showed the highest ZT values of 0.75, 0.74, and 4.8, respectively, at near‐room temperature. Furthermore, numerous novel thermoelectric generator‐powered wearable health monitoring sensors and Internet of Things devices are also presented. Finally, the current challenges and perspectives on the future development of thermoelectric generator, in particular for efficient materials and self‐powered devices, are also discussed.
Thermoelectric generators have enormous potential to power wearables and portable electronics. This paper presents an overview of the thermoelectric energy generation mechanism, and covers state‐of‐the‐art inorganic and organic thermoelectric materials as well as emerging applications such as wearable sensors, biomedical devices, and the Internet of Things devices. Current challenges and perspectives for future progress of thermoelectric generators are also discussed.
Photodetectors with broadband detection capability are desirable for sensing applications in the coming age of the internet‐of‐things. Although 2D layered materials (2DMs) have been actively pursued ...due to their unique optical properties, by far only graphene and black arsenic phosphorus have the wide absorption spectrum that covers most molecular vibrational fingerprints. However, their reported responsivity and response time are falling short of the requirements needed for enabling simultaneous weak‐signal and high‐speed detections. Here, a novel 2DM, black phosphorous carbide (b‐PC) with a wide absorption spectrum up to 8000 nm is synthesized and a b‐PC phototransistor with a tunable responsivity and response time at an excitation wavelength of 2004 nm is demonstrated. The b‐PC phototransistor achieves a peak responsivity of 2163 A W−1 and a shot noise equivalent power of 1.3 fW Hz−1/2 at 2004 nm. In addition, it is shown that a response time of 0.7 ns is tunable by the gating effect, which renders it versatile for high‐speed applications. Under the same signal strength (i.e., excitation power), its performance in responsivity and detectivity in room temperature condition is currently ahead of recent top‐performing photodetectors based on 2DMs that operate with a small bias voltage of 0.2 V.
A black phosphorus carbide infrared phototransistor with a wide absorption spectrum that spans most molecular vibrational fingerprints up to 8000 nm is realized. The device achieves a peak responsivity of 2163 A W−1 and a shot noise equivalent power of 1.3 fW Hz−1/2 at 2004 nm along with a gate‐tunable response time of 0.7 ns, making it versatile for sensing applications in future Internet‐of‐Things.
Cellular senescence, a state of irreversible growth arrest triggered by various stressors, engages in a category of pathological processes, whereby senescent cells accumulate in mitotic tissues. ...Senolytics as novel medicine against aging and various diseases through the elimination of senescent cells has emerged rapidly in recent years. Exercise is a potent anti‐aging and anti‐chronic disease medicine, which has shown the capacity to lower the markers of cellular senescence over the past decade. However, whether exercise is a senolytic medicine for aging and various diseases remains unclear. Here, we have conducted a systematic review of the published literature studying the senolytic effects of exercise or physical activity on senescent cells under various states in both human and animal models. Exercise can reduce the markers of senescent cells in healthy humans, while it lowered the markers of senescent cells in obese but not healthy animals. The discrepancy between human and animal studies may be due to the relatively small volume of research and the variations in markers of senescent cells, types of cells/tissues, and health conditions. These findings suggest that exercise has senolytic properties under certain conditions, which warrant further investigations.
Cellular senescent upregulates certain markers such as p16INK4a, p21Cip1, SA‐beta‐Gal, and/or SASP, which can also be shown in accelerated aging animals. This review discusses by using human and experimental laboratory animal studies to show how exercise attenuates all these senescent markers and serves as senolytic medicine.
Chronic kidney disease (CKD) is a dangerous ailment that can last a person’s entire life and is caused by either kidney malignancy or decreased kidney functioning. It is feasible to halt or slow the ...progression of this chronic disease to an end-stage wherein dialysis or surgical intervention is the only method to preserve a patient’s life. Earlier detection and appropriate therapy can increase the likelihood of this happening. Throughout this research, the potential of several different machine learning approaches for providing an early diagnosis of CKD has been investigated. There has been a significant amount of research conducted on this topic. Nevertheless, we are bolstering our approach by making use of predictive modeling. Therefore, in our approach, we investigate the link that exists between data factors as well as the characteristics of the target class. We are capable of constructing a collection of prediction models with the help of machine learning and predictive analytics, thanks to the better measures of attributes that can be introduced using predictive modeling. This study starts with 25 variables in addition to the class property, but by the end, it has narrowed the list down to 30% of those parameters as the best subset to identify CKD. Twelve different machine learning-based classifiers have been tested in a supervised learning environment. Within the confines of a supervised learning environment, a total of 12 different machine learning-based classifiers have indeed been examined, with the greatest performance indicators being an accuracy of 0.983, a precision of 0.98, a recall of 0.98, and an F1-score of 0.98 for the XgBoost classifier. The way the research was done leads to the conclusion that recent improvements in machine learning, along with the help of predictive modeling, make for an interesting way to find new solutions that can then be used to test the accuracy of prediction in the field of kidney disease and beyond.
IntroductionBoth doctors and nurses showed a greater risk of being exposed to different mental health conditions following mass casualties. This systematic review aims to synthesise the existing ...evidence on the prevalence of anxiety, depression and post-traumatic stress disorder and their associated risk factors among doctors and nurses following mass casualty incidents.Methods and analysisSeven electronic databases (PubMed, PsycINFO, MEDLINE Ovid, Embase, CINAHL, Web of Science and Nursing & Allied Health database) will be searched from 2010 to 2022 with peer-reviewed articles in English language using the predefined keywords. Two reviewers will independently screen the titles and abstracts, as well as review the full texts using the eligibility criteria, then extract data independently. The National Institutes of Health Quality Assessment Tools (NIH-QAT) for quantitative studies, the Critical Appraisal Skills Programme (CASP) Checklist for qualitative studies and the Mixed-Methods Appraisal Tool (MMAT) for mixed-method studies will be used to measure the quality appraisal of eligible studies. A third reviewer will resolve the discrepancies when the two reviewers cannot reach an agreement in any step. The result from the eligible studies will be described following narrative synthesis with the key characteristics and findings of the included studies, and meta-analysis will be performed, if applicable.Ethics and disseminationThis systematic review deals with existing published studies without any personally identifiable information of participants. Therefore, ethical approval from the research committee is not required. Findings from this review will be disseminated in peer-reviewed journals and presented at relevant international conferences.PROSPERO registration numberCRD42023412852.
E-learning is a relatively trending system of education that has been placed over conventional campus-based learning worldwide, especially since the emergence of the COVID-19 pandemic. This study ...aimed to assess e-learning readiness among university students of a developing country like Bangladesh and identify the independent predictors of e-learning readiness. From 26 December 2020 to 11 January 2021, a total of 1162 university students who had enrolled for e-learning completed a semi-structured questionnaire. Data were collected online via "Google Form" following the principles of snowball sampling through available social media platforms in Bangladesh. A multivariable linear regression model was fitted to investigate the association of e-learning readiness with perceived e-learning stress and other independent predictor variables. A total of 1162 university students participated in this study. The results indicated that with the increase of students' perceived e-learning stress score, the average e-learning readiness score was significantly decreased (beta = -0.43, 95% CI: -0.66, -0.20). The students did not seem ready, and none of the e-learning readiness scale items reached the highest mean score (5.0). The age, gender, divisional residence, preference of students and their parents, devices used, and having any eye problems were significantly associated with the students' e-learning readiness. During the prolonged period of the COVID-19 pandemic, e-learning implication strategies are needed to be assessed systematically with the level of readiness and its' impacts among students for the continuation of sound e-learning systems. The study findings recommend evaluating the e-learning readiness of university students and the mental health outcomes during the COVID-19 catastrophe in Bangladesh.
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